GitLab

* handle aliases centralized

* convert aliases dict to class

* Bug fix for first_metric_only if the first metric is train metric.

* Update bug fix for feval issue.

* Disable feval for first_metric_only.

* Additional test items.

* Fix wrong assertEqual settings & formating.

* Change dataset of test.

* Fix random seed for test.

* Modiry assumed test result due to different sklearn verion between CI and local.

* Remove f-string

* Applying variable  assumed test result for test.

* Fix flake8 error.

* Modifying  in accordance with review comments.

* Modifying for pylint.

* simplified tests

* Deleting error criteria `if eval_metric is None`.

* Delete test items of classification.

* Simplifying if condition.

* Applying first_metric_only for sklearn wrapper.

* Modifying test_sklearn for comforming to python 2.x

* Fix flake8 error.

* Additional fix for sklearn and add tests.

* Bug fix and add test cases.

* some refactor

* fixed lint

* fixed lint

* Fix duplicated metrics scores to pass the test.

* Fix the case first_metric_only not in params.

* Converting metrics aliases.

* Add comment.

* Modify comment for pylint.

* Modify comment for pydocstyle.

* Using split test set for two eval_set.

* added test case for metric aliases and length checks

* minor style fixes

* fixed rmse name and alias position

* Fix the case metric=[]

* Fix using env.model._train_data_name

* Fix wrong test condition.

* Move initial process to _init() func.

* Modify test setting for test_sklearn & training data matching on callback.py

* test_sklearn.py
-> A test case for training is wrong, so fixed.

* callback.py
-> A condition of if statement for detecting test dataset is wrong, so fixed.

* Support composite name metrics.

* Remove metric check process & reduce redundant test cases.

For #2273 fixed not only the order of metrics in cpp, removing metric check process at callback.py

* Revised according to the matters pointed out on a review.

* increased code readability

* Fix the issue of order of validation set.

* Changing to OrderdDict from default dict for score result.

* added missed check in cv function for first_metric_only and feval co-occurrence

* keep order only for metrics but not for datasets in best_score

* move OrderedDict initialization to init phase

* fixed minor printing issues

* move first metric detection to init phase and split can be performed without checks

* split only once during callback

* removed excess code

* fixed typo in variable name and squashed ifs

* use setdefault

* hotfix

* fixed failing test

* refined tests

* refined sklearn test

* Making "feval" effective on early stopping.

* allow feval and first_metric_only for cv

* removed unused code

* added tests for feval

* fixed printing

* add note about whitespaces in feval name

* Modifying final iteration process in case valid set is  training data.

* Some basic changes to the plot of the trees to make them readable.

* Squeezed the information in the nodes.

* Added colouring when a dictionnary mapping the features to the constraints is passed.

* Fix spaces.

* Added data percentage as an option in the nodes.

* Squeezed the information in the leaves.

* Important information is now in bold.

* Added a legend for the color of monotone splits.

* Changed "split_gain" to "gain" and "internal_value" to "value".

* Sqeezed leaves a bit more.

* Changed description in the legend.

* Revert "Sqeezed leaves a bit more."

This reverts commit dd8bf14a3ba604b0dfae3b7bb1c64b6784d15e03.

* Increased the readability for the gain.

* Tidied up the legend.

* Added the data percentage in the leaves.

* Added the monotone constraints to the dumped model.

* Monotone constraints are now specified automatically when plotting trees.

* Raise an exception instead of the bug that was here before.

* Removed operators on the branches for a clearer design.

* Small cleaning of the code.

* Setting a monotone constraint on a categorical feature now returns an exception instead of doing nothing.

* Fix bug when monotone constraints are empty.

* Fix another bug when monotone constraints are empty.

* Variable name change.

* Added is / isn't on every edge of the trees.

* Fix test "tree_create_digraph".

* Add new test for plotting trees with monotone constraints.

* Typo.

* Update documentation of categorical features.

* Typo.

* Information in nodes more explicit.

* Used regular strings instead of raw strings.

* Small refactoring.

* Some cleaning.

* Added future statement.

* Changed output for consistency.

* Updated documentation.

* Added comments for colors.

* Changed text on edges for more clarity.

* Small refactoring.

* Modified text in leaves for consistency with nodes.

* Updated default values and documentaton for consistency.

* Replaced CHECK with Log::Fatal for user-friendliness.

* Updated tests.

* Typo.

* Simplify imports.

* Swapped count and weight to improve readibility of the leaves in the plotted trees.

* Thresholds in bold.

* Made information in nodes written in a specific order.

* Added information to clarify legend.

* Code cleaning.

* fixed class_weight

* fixed lint

* added test

* hotfix

* Update sklearn.py

* Update parameter_generator.py

[python] fixed picklability of sklearn models with custom obj and updated docstings for custom obj (#2191)

* refactored joblib test

* fixed picklability of sklearn models with custom obj and updated docstings for custom obj

* pickled model should be able to predict without refitting

* PR #1879

* Update docs with parameter_generator.py

* Update wrapper doc for sklearn

* added ability to pass first_metric_only in params

* simplified tests

* fixed test

* fixed punctuation

* ignore pandas ordered categorical columns by default

* fix tests

* fix tests

* added comments

* Update doc string for pred_contrib

See comments at the end of #1969

* Update basic.py

* Update basic.py

* update doc strings

* update equals sign in doc string

* strip whitespace and gen rst

* strip whitespace

* Use first_metric_only flag for early_stopping function.

In order to apply early stopping with only first metric, applying first_metric_only flag for early_stopping function.

* upcate comment

* Revert "upcate comment"

This reverts commit 1e75a1a415cc16cfbe795181e148ebfe91469be4.

* added test

* fixed docstring

* cut comment and save one line

* document new feature

* convert datatable to numpy directly

* fix according to comments

* updated more docstrings

* simplified isinstance check

* Update compat.py

* [python] fix creating train_set in fit

https://github.com/Microsoft/LightGBM/blob/cc99f0d36ae929eb02b22a072823ab7c6d3155ab/python-package/lightgbm/sklearn.py#L519
may False even if valid_data[0] is X and valid_data[1] is y actually, because `check_X_y` might return copy of X and y.
https://scikit-learn.org/0.20/modules/generated/sklearn.utils.check_X_y.html

cf. https://github.com/Microsoft/LightGBM/pull/451

* use assertIn

* added docstring style test and fixed errors in existing docstrings

* hotfix

* hotfix

* fix grammar

* hotfix

* Fixed some typos in Python API Docs

* FixTypo changed validation set -> sets

* fixed FutureWarning about cv default value

* fixed according to new check_estimator API

* fixed joblib warning

* break extremely large lines in basic.py

* break extremely large lines in callback.py

* break extremely large lines in engine.py

* break extremely large lines in sklearn.py

* hotfixes

* warning on categorical feature with sparse values

* [docs] categorical features note

* pass params to _InnerPredictor in train and cv

* fixed verbosity param description

* treat silent param as Fatal log level

* create Dataset in refit method silently

* do not overwrite verbose param by silent argument

* bring consistency and clearness into early_stopping_rounds desc, metric desc and implementation

* hotfix

* hotfix

* used NDCG as default metric for lambdarank task

* fixed missed methods at ReadTheDocs and changed default eval_metric

* leaved only unique metrics

* fixed comment

* refined num_iteration argument in python

* hotfix

* broadcast info about negative values in categorical features to python package

* update link to categorical_feature parameter

* refined random_state description

* use neutral alias and fixed desc of eval_at param, since metric can be not only ndcg

* simplified checks

* consider verbose alias

* fixed declaring function every loop iteration

* added new aliases for params

* run helper/parameter_generator.py

* removed useless test

* ignore vim temporary files

* add importance_type arg to sklearn API

* update documentation info

* remote a trailing space

* remove trailing space (again :))

* add instructions on importance choices to sklearn API

* drop mention of constructor in the feature type setting

* adding a test for different feture types

* remove trailing spaces, make shorter assert in feature importance type handling test

* fixing style issue introduced with the new test